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Summary Fossilized plant–insect herbivore associations provide fundamental information about the assembly of terrestrial communities through geologic time. However, fossil evidence of associations originating in deep time and persisting to the modern day is scarce.We studied the insect herbivore damage found on 284Eucalyptus frenguellianaleaves from the early Eocene Laguna del Hunco rainforest locality in Argentinean Patagonia and compared damage patterns with those observed on extant, rainforest‐associatedEucalyptusspecies from Australasia (> 10 000 herbarium sheets reviewed).In the fossil material, we identified 28 insect herbivory damage types, including 12 types of external feeding, one of piercing‐and‐sucking, five of galls, and 10 of mines. All 28 damage types were observed in the herbarium specimens.The finding of all the fossil damage types on extantEucalyptusspecimens suggests long‐standing associations between multiple insect herbivore lineages and their host genus spanning 52 million years across the Southern Hemisphere. This long‐term persistence, probably enabled through niche conservatism in wet eucalypt forests, demonstrates the imprint of fossil history on the composition of extant insect herbivore assemblages. Although the identities of most insect culprits remain unknown, we provide a list ofEucalyptusspecies and specific population locations to facilitate their discovery, highlighting the relevance of fossils in discovering extant biodiversity.
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Applying machine learning to investigate long‐term insect–plant interactions preserved on digitized herbarium specimens
PremiseDespite the economic significance of insect damage to plants (i.e., herbivory), long‐term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant–insect interactions during the Anthropocene. MethodsWe initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens by training an insect damage detector and a damage type classifier on two distantly related plant species (Quercus bicolorandOnoclea sensibilis). We experimented with (1) classifying six types of herbivory and two control categories of undamaged leaf, and (2) detecting two of the damage categories for which several hundred annotations were available. ResultsDamage detection results were mixed, with a mean average precision of 45% in the simultaneous detection and classification of two types of damage. However, damage classification on hand‐drawn boxes identified the correct type of herbivory 81.5% of the time in eight categories. The damage classifier was accurate for categories with 100 or more test samples. DiscussionThese tools are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models, allowing researchers to extract similar data and apply them to biological hypotheses.
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- Award ID(s):
- 1909821
- PAR ID:
- 10456797
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Applications in Plant Sciences
- Volume:
- 8
- Issue:
- 6
- ISSN:
- 2168-0450
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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